基于输出约束的电液伺服系统非线性方法研究

 针对于电液伺服系统的输出约束问题，提出了将反步控制和障碍李雅普诺夫函数相结合的控制方法来设计控制律。障碍李雅普诺夫函数在解决非线性系统中的状态和输出约束上有较为突出的贡献，当状态或者输出约束达到一定的约束限制的时候，整个函数就会趋于无穷大，确保了在系统运动过程中约束限制被破坏的可能。通过构造关于状态变量和期望值误差的方法，得到了确保系统能够渐进稳定跟踪期望的3个控制律，并保证系统在李雅普诺夫议意义下是稳定的。最后，通过数字仿真验证了该方法的可行性。     

Robust adaptive motion/force control scheme for crawler-type mobile manipulator with nonholonomic constraint based on sliding mode control approach

In this paper, a robust adaptive motion/force control (RAMFC) scheme is presented for a crawler-type mobile manipulator (CTMM) with nonholonomic constraint. For the position tracking control design, an adaptive sliding mode tracking controller is proposed to deal with the unknown upper bounds of system parameter uncertainties and external disturbances. Based on the position tracking results, a robust control strategy is also developed for the nonholonomic constraint force of CTMM. According to the Lyapunov stability theory, the stability of the closed-loop control system, the uniformly ultimately boundedness of position tracking errors, and the boundedness of the force error and adaptive coefficient errors are all guaranteed by using the derived RAMFC scheme. Simulation and experimental tests on a CTMM with two-link manipulator demonstrate the effectiveness and robustness of the proposed control scheme.     

输入受限无人帆船自适应航向跟踪控制

针对带有输入受限且存在未知控制系数约束情形下,考虑了具有未知模型和风、浪等外界干扰的无人船航向跟踪控制问题,提出一种受限控制输入约束下的跟踪控制方法。该方法运用RBF神经网络对未知模型进行在线逼近,利用Nussbaum自适应增益技术解决未知控制系数问题。根据滑模控制理论,设计具有指数趋近律的鲁棒控制项,保证所得误差闭环系统快速响应且最终趋向0。为弱化传统滑模控制产生的抖振问题,将符号函数替换成饱和函数使控制输入变得平滑。引入一种误差辅助系统,构建了帮助误差闭环系统输入退出饱和机制。通过李雅普诺夫稳定性理论,给出了跟踪控制方法的稳定性数学分析过程,证明误差闭环跟踪控制系统的所有信号最终一致有界性。最后通过仿真结果验证了所得理论的有效性。     

自主式水下机器人自适应区域跟踪控制

研究自主式水下机器人的区域跟踪控制问题,提出一种基于PD神经滑模的自适应区域跟踪控制方法。针对自主式水下机器人自适应控制器中仅在线调整网络权值的径向基函数神经网络存在收敛性能差的问题,给出同时对径向基函数神经网络权值、径向基函数中心与方差进行自适应调整的方法,使径向基函数神经网络无须离线选取径向基函数中心与方差,即可进行在线自适应学习。考虑到控制器中滑模控制项易引起系统抖振的问题,提出一种基于指数函数的滑模切换增益调节方法,使滑模切换增益能够依据跟踪误差实时调节以降低系统抖振。基于Lyapunov理论对所提自适应区域跟踪控制方法的稳定性进行分析。通过自主式水下机器人的仿真试验与水池试验验证所提方法的有效性。     

Development of an adaptive radial basis function neural network estimator-based continuous sliding mode control for uncertain nonlinear systems

In this paper an adaptive neural network (NN)-based nonlinear controller is proposed for trajectory tracking of uncertain nonlinear systems. The adopted control algorithm combines a continuous second-order sliding mode control (CSOSMC), the radial basis function neural network (RBFNN) and the adaptive control methodology. First, a second-order sliding mode control scheme (SOSMC), which is published recently in literature for linear uncertain systems, is extended for nonlinear uncertain systems. Second, an adaptive radial basis function neural network estimator-based continuous second order sliding mode control algorithm (CSOSMC-ANNE) is adopted. In CSOSMC-ANNE control methodology, a radial basis function neural network with adaptive parameters is exploited to approximate the unknown system parameters and improve performance against perturbations. Also, the discontinuous switching control of SOSMC is supplanted with a smooth continuous control action to completely eliminate the chattering phenomenon. The convergence and global stability of the closed-loop system are proved using Lyapunov stability method. Numerical computer simulations, with dynamical model of the nonlinear inverted pendulum system, are presented to demonstrate the effectiveness and advantages of the presented control scheme.     

A robust and adaptive force/position control for two cooperating robot arms under uncertainty

This paper presents a motion coordination of a two-cooperating robot arm when there are unknown system parameters and bounded input disturbances. The order of the model of the two-arm system is reduced. To control this, a force/position control scheme based on an inverse dynamics control scheme is devised. On the top of the control scheme, an adaptive control scheme to take care of parametric uncertainties, and a robust control scheme to compensate coupling forces between two arms and input disturbances are devised. The adaptive and the robust control scheme are derived based on a devised Lyapunov function. The adaptive control algorithm is practical since it does not require the feedback of the second derivative of joint angles and interacting forces. The robust control scheme guarantees that the tracking error of the leader arm and the interacting forces between two arms are confined in a certain region. Numerical examples using dual 3 degree of freedom robot arm are shown.     

Sliding mode control based impact angle control guidance considering the seeker׳s field-of-view constraint

The problem of impact angle control guidance for a field-of-view constrained missile against non-maneuvering or maneuvering targets is solved by using the sliding mode control theory. The existing impact angle control guidance laws with field-of-view constraint are only applicable against stationary targets and most of them suffer abrupt-jumping of guidance command due to the application of additional guidance mode switching logic. In this paper, the field-of-view constraint is handled without using any additional switching logic. In particular, a novel time-varying sliding surface is first designed to achieve zero miss distance and zero impact angle error without violating the field-of-view constraint during the sliding mode phase. Then a control integral barrier Lyapunov function is used to design the reaching law so that the sliding mode can be reached within finite time and the field-of-view constraint is not violated during the reaching phase as well. A nonlinear extended state observer is constructed to estimate the disturbance caused by unknown target maneuver, and the undesirable chattering is alleviated effectively by using the estimation as a compensation item in the guidance law. The performance of the proposed guidance law is illustrated with simulations.     

Command filter based adaptive control of asymmetric output-constrained switched stochastic nonlinear systems

In this paper, adaptive tracking control problem is investigated for a class of switched stochastic nonlinear systems with an asymmetric output constraint. By introducing a nonlinear mapping (NM), the asymmetric output-constrained switched stochastic system is first transformed into a new system without any constraint, which achieves the equivalent control objective. The command filter technique is employed to handle the “explosion of complexity” in traditional backstepping design, and neural networks (NNs) are directly utilized to cope with the completely unknown nonlinear functions and stochastic disturbances existing in systems. At last, on the basis of stochastic Lyapunov function method, an adaptive neural controller is developed for the considered system. It is shown that the designed adaptive controller can guarantee that all the signals remain semi-globally uniformly ultimately bounded (SGUUB), while the output constraint is satisfied and the desired signal can be tracked with a small domain of the origin. Simulation results are offered to illustrate the feasibility of the newly designed control scheme.     

Robust adaptive precision motion control of hydraulic actuators with valve dead-zone compensation

This paper addresses the high performance motion control of hydraulic actuators with parametric uncertainties, unmodeled disturbances and unknown valve dead-zone. By constructing a smooth dead-zone inverse, a robust adaptive controller is proposed via backstepping method, in which adaptive law is synthesized to deal with parametric uncertainties and a continuous nonlinear robust control law to suppress unmodeled disturbances. Since the unknown dead-zone parameters can be estimated by adaptive law and then the effect of dead-zone can be compensated effectively via inverse operation, improved tracking performance can be expected. In addition, the disturbance upper bounds can also be updated online by adaptive laws, which increases the controller operability in practice. The Lyapunov based stability analysis shows that excellent asymptotic output tracking with zero steady-state error can be achieved by the developed controller even in the presence of unmodeled disturbance and unknown valve dead-zone. Finally, the proposed control strategy is experimentally tested on a servovalve controlled hydraulic actuation system subjected to an artificial valve dead-zone. Comparative experimental results are obtained to illustrate the effectiveness of the proposed control scheme.     

Optimal adaptive interval type-2 fuzzy fractional-order backstepping sliding mode control method for some classes of nonlinear systems

In this research, a novel adaptive interval type-2 fuzzy fractional-order backstepping sliding mode control (AIT2FFOBSMC) method is presented for some classes of nonlinear fully-actuated and under-actuated mechanical systems with uncertainty. The AIT2FFOBSMC method exploits the advantages of backstepping and sliding mode methods to improve the performance of closed-loop control systems by lowering the tracking error and increasing robustness. To mitigate chattering and the tracking error, a fractional sliding surface is designed. In addition to the fractional sliding surface, an adaptive interval type-2 fuzzy compensator is used to estimate the uncertainty and perturbation of the nonlinear system in order to further reduce chattering caused by switching term as well as to enhance the perturbation rejection. In order to achieve an optimal performance, the multi-tracker optimization algorithm (MTOA) is used. Finally, a number of simulations and experimental tests are carried out to examine the performance of the AIT2FFOBSMC method.     

Sliding mode control with fuzzy adaptive perturbation compensator for 6-DOF parallel manipulator

This paper proposes a sliding mode controller with fuzzy adaptive perturbation compensator (FAPC) to get a good control performance and reduce the chatter. The proposed algorithm can reduce the chattering because the proposed fuzzy adaptive perturbation compensator compensates the perturbation terms. The compensator computes the control input for compensating unmodeled dynamic terms and disturbance by using the observer-based fuzzy adaptive network (FAN). The weighting parameters of the compensator are updated by on-line adaptive scheme in order to minimize the estimation error and the estimation velocity error of each actuator. Therefore, the combination of sliding mode control and fuzzy adaptive network gives the robust and intelligent routine to get a good control performance. To evaluate the control performance of the proposed approach, tracking control is experimentally carried out for the hydraulic motion platform which consists of a 6-DOF parallel manipulator.     

Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems

This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator.     

Adaptive NN control for discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints

This paper focuses on the problem of adaptive neural network tracking control for a class of discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints. Two novel state-feedback and output-feedback dynamic control laws are established where the function tanh() is employed to solve the saturation constraint problem. Implicit function theorem and mean value theorem are exploited to deal with non-affine variables that are used as actual control. Radial basis function neural networks are used to approximate the desired input function. Discrete Nussbaum gain is used to estimate the unknown sign of control gain. The uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. A simulation example is provided to illustrate the effectiveness of control schemes proposed in this paper.     

Mean deviation coupling synchronous control for multiple motors via second-order adaptive sliding mode control

A new mean deviation coupling synchronization control strategy is developed for multiple motor control systems, which can guarantee the synchronization performance of multiple motor control systems and reduce complexity of the control structure with the increasing number of motors. The mean deviation coupling synchronization control architecture combining second-order adaptive sliding mode control (SOASMC) approach is proposed, which can improve synchronization control precision of multiple motor control systems and make speed tracking errors, mean speed errors of each motor and speed synchronization errors converge to zero rapidly. The proposed control scheme is robustness to parameter variations and random external disturbances and can alleviate the chattering phenomena. Moreover, an adaptive law is employed to estimate the unknown bound of uncertainty, which is obtained in the sense of Lyapunov stability theorem to minimize the control effort. Performance comparisons with master–slave control, relative coupling control, ring coupling control, conventional PI control and SMC are investigated on a four-motor synchronization control system. Extensive comparative results are given to shown the good performance of the proposed control scheme.     

冗余自由度仿人机械臂自适应迭代学习控制研究

在深入研究自适应迭代学习控制理论、七自由度乒乓球机械臂动力学模型及轨迹规划的基础上,提出将改进后的自适应迭代学习控制算法运用到带有重复时变干扰的冗余自由度机械臂上。该控制系统旨在实现两大目标：一是使乒乓球机械臂准确快速地跟踪参考轨迹并在末点达到指定的击球速度;二是引入饱和函数减小输入转矩的抖振。Lyapunov理论分析及MATLAB仿真验证了整个控制系统的有效性：当迭代次数增加时,跟踪误差关于有限时间区间内一致收敛到零;加快迭代学习的收敛速度,并消除抖振。     

基于屏障 Lyapunov 函数的上肢康复机器人 自适应主动交互训练控制

针对上肢运动功能障碍患者进行辅助康复训练,搭建了一套上肢康复外骨骼机器人系统,并提出一种基于屏障Lyapunov函数的增广神经网络自适应导纳控制策略。首先,介绍了上肢康复外骨骼的机械机构及其控制系统。然后,推演了控制器的设计过程并进行了Lyapunov稳定性证明。最后,分别进行了不同控制内环的轨迹跟踪被动训练实验和不同导纳参数下基于人机交互力的主动交互训练实验,同时分析比对了主动训练时的人机交互力与轨迹偏差的变化关系。被动训练实验结果证明了增广神经网络对人机模型动力学的逼近效果,其轨迹跟踪峰值误差为模糊PID控制器的53%。主动交互训练实验证明了通过调整导纳参数可实现在相同训练任务下不同强度的康复训练以匹配不同康复阶段下的患者。     

一种基于RBF补偿的自适应复合同步控制

针对Linapod并联机器人的同步轨迹跟踪问题,提出一种新的自适应复合同步控制策略。以Linapod 机构为研究对象,设计了一种将前馈控制、PD控制以及RBF适配器补偿相结合的复合同步控制策略,并根据李雅普洛夫稳定性条件给出了RBF适配器权值更新率,从而保证了控制系统的运动稳定性。研究结果表明,在该控制器的作用下,当向各轴加入单位正弦信号干扰和不加入干扰时,各轴跟踪误差都能小于0.003 mm,从而验证了该控制策略的有效性和正确性。     

Robust tracking and distributed synchronization control of a multi-motor servomechanism with H-infinity performance

The multi-motor servomechanism (MMS) is a multi-variable, high coupling and nonlinear system, which makes the controller design challenging. In this paper, an adaptive robust H-infinity control scheme is proposed to achieve both the load tracking and multi-motor synchronization of MMS. This control scheme consists of two parts: a robust tracking controller and a distributed synchronization controller. The robust tracking controller is constructed by incorporating a neural network (NN) K-filter observer into the dynamic surface control, while the distributed synchronization controller is designed by combining the mean deviation coupling control strategy with the distributed technique. The proposed control scheme has several merits: 1) by using the mean deviation coupling synchronization control strategy, the tracking controller and the synchronization controller can be designed individually without any coupling problem; 2) the immeasurable states and unknown nonlinearities are handled by a NN K-filter observer, where the number of NN weights is largely reduced by using the minimal learning parameter technique; 3) the H-infinity performances of tracking error and synchronization error are guaranteed by introducing a robust term into the tracking controller and the synchronization controller, respectively. The stabilities of the tracking and synchronization control systems are analyzed by the Lyapunov theory. Simulation and experimental results based on a four-motor servomechanism are conducted to demonstrate the effectiveness of the proposed method.     

基于误差反馈学习的永磁同步电机变负载有限时间速度控制

针对传统永磁同步电机调速系统面对变负载和大范围调速时,P、I参数需要频繁调整且速度跟踪不理想的问题,提出了一种基于误差反馈学习结构的永磁同步电机有限时间速度控制方法。在对永磁同步电机运动方程分析的基础上,使用非线性PI和径向基神经网络建立了速度环控制器模型。前者保证控制系统收敛和稳定,其输出作为神经网络的误差学习参数;后者基于终端滑模理论设计参数调整律,加快神经网络的参数收敛速度,使得神经网络的输出逐渐取代非线性PI成为控制系统的主要控制器。利用李雅普诺夫稳定判据分析了控制器的收敛性,并在永磁同步电机调速系统上进行了试验。研究结果表明,基于误差反馈学习结构的有限时间控制策略能够减小系统静态误差和抖振,具有一定的抗干扰能力。     

具有状态约束的机械臂切换自适应控制

为了解决具有状态约束的机械臂的控制问题,本文针对一类具有全状态约束和状态不完全可测的切换严格反馈非线性系统进行研究,通过引入状态观测器、自适应神经网络和动态表面控制技术,设计了一种基于径向基函数(RBF)神经网络的自适应输出反馈控制方法。利用Lyapunov方法和平均驻留时间理论(ADT)保证了闭环系统所有信号是半全局一致最终有界的(SGUUB),通过数值例子仿真验证了所提方法的有效性。最后将该方法应用于带电机驱动的机械臂并进行仿真实验,仿真结果表明,机械臂轨迹跟踪误差很小,有着良好的控制精度,同时也表明所提出的控制算法能够应用于实际工程模型。